基于生成路由金字塔的细胞实例无监督学习
Unsupervised Learning of Cell Instances with Generative Routing Pyramids
浏览论文内容
中文总结 AI 辅助
该研究提出基于生成路由金字塔的无监督方法，实现未标注显微镜图像的细胞实例分割与表型分类，在实例分割及细胞表型生成建模上表现具竞争力。
中文摘要 AI 辅助
识别和表示细胞或细胞核等对象实例是显微镜图像分析中的常见任务。已有的机器学习工作流通常采用监督检测或分割，随后进行特征提取或分类，这需要手动标注，并将实例分割和细胞表示视为独立阶段。我们提出一种新的无监督方法，用于从未标注的显微镜图像中进行细胞实例分割和表型分类。该方法基于使用由空间稀疏潜在源关联像素的粗到细路由金字塔来重建每个图像，得到的像素到潜在关联生成实例掩码，而源潜在则编码细胞形态。我们在不同细胞形态和成像模态的实例分割中展示了具有竞争力的性能，还能对扰动下的细胞表型进行生成建模。源代码和检查点可在指定URL获取。
英文摘要
Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell representation as separate stages. We describe a new unsupervised method for cell instance segmentation and phenotypic classification from unlabeled microscopy images. Our method is based on reconstructing each image using a coarse-to-fine routing pyramid that associates pixels with spatially sparse latent sources. The resulting pixel-to-latent associations yield instance masks, while the source latents encode cell morphology. We demonstrate competitive performance in instance segmentation across diverse cell morphologies and imaging modalities, as well as generative modeling of cellular phenotypes under perturbations. Source code and checkpoints are available at https://github.com/weigertlab/routing-pyramids.
发表机构
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））
- Technische Universität Dresden（德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。
